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In the face of environmental degradation, sustainable development has become a common goal across the globe. Making a scientifically based investment scheme is of great significance to promote the sustainable development of China's economy. However, there is scarce research related to such an investment scheme of sustainable development. This paper proposes a new inverse data envelopment analysis method with undesirable outputs to make several scientifically based investment schemes from different perspectives, namely, the natural, regulation, and optimal perspectives. By this method, decision makers can scientifically forecast the specific amount of investment based on their actual sustainable development objectives, which is conducive for reducing the blindness of investment in the future. In addition, a new ideal perspective is defined to guide a definite direction for improving the level of sustainable development. Combined with the gray forecasting model GM(1,1), the methods proposed by this paper were then applied to analyze the investment problem for China's sustainable development during the 2015–2024 period. The results show that: the unbalanced distribution of labor investment and the excessive investment in capital and energy are serious barriers to China's sustainable development in the short term; and in the long term, the demand for investment in labor and capital will continue to increase along with a lower demand for energy investment, and that appropriately strengthening environmental regulations will not affect the overall demand for investment. Meanwhile, improvement directions for improving China's sustainable development are discussed, and the results show that most of developing and undeveloped regions in China have great potential for improvement. Finally, some suggestions are proposed in order to create better conditions for China's sustainable development. [ABSTRACT FROM AUTHOR]

Using panel data from 2003 to 2016, this paper estimates the green development efficiency of 34 cities in Northeast China based on the SBM-DEA model and determines whether industrial agglomeration promotes or impedes green development efficiency. The results show the following: (1) The average green development efficiency of Northeast China exhibits a fluctuant upward trend in 2003–2016, and there are significant spatial differences. (2) During the revitalization period, industrial agglomeration hinders green development efficiency. A U-shaped relationship exists between industrial agglomeration and green development. (3) Economic development level and foreign direct investment are positively related to green development efficiency, while industrial structure, energy consumption structure and environmental regulation are negatively related. Economic development level has the greatest positive influence and environmental regulation the strongest negative influence on green development. Several corresponding policy implications are proposed for promoting green development and revitalization. • The average green development efficiency of Northeast China exhibits a fluctuant upward trend. • Significant spatial differences exist in the green development efficiency in Northeast China. • A U-shaped relationship exists between industrial agglomeration and green development. • Economic development has the greatest positive effect on the green development. • Environmental regulation has the strongest negative influence on green development. [ABSTRACT FROM AUTHOR]